MRET: Modified Recursive Elimination Technique for ranking author assessment parameters.

In scientific research, assessing the impact and influence of authors is crucial for evaluating their scholarly contributions. Whereas in literature, multitudinous parameters have been developed to quantify the productivity and significance of researchers, including the publication count, citation count, well-known h index and its extensions and variations. However, with a plethora of available assessment metrics, it is vital to identify and prioritize the most effective metrics. To address the complexity of this task, we employ a powerful deep learning technique known as the Multi-Layer Perceptron (MLP) classifier for the classification and the ranking purposes. By leveraging the MLP's capacity to discern patterns within datasets, we assign importance scores to each parameter using the proposed modified recursive elimination technique. Based on the importance scores, we ranked these parameters. Furthermore, in this study, we put forth a comprehensive statistical analysis of the top-ranked author assessment parameters, encompassing a vast array of 64 distinct metrics. This analysis gives us treasured insights in between these parameters, shedding light on the potential correlations and dependencies that may affect assessment outcomes. In the statistical analysis, we combined these parameters by using seven well-known statistical methods, such as arithmetic means, harmonic means, geometric means etc. After combining the parameters, we sorted the list of each pair of parameters and analyzed the top 10, 50, and 100 records. During this analysis, we counted the occurrence of the award winners. For experimental proposes, data collection was done from the field of Mathematics. This dataset consists of 525 individuals who are yet to receive their awards along with 525 individuals who have been recognized as potential award winners by certain well known and prestigious scientific societies belonging to the fields' of mathematics in the last three decades. The results of this study revealed that, in ranking of the author assessment parameters, the normalized h index achieved the highest importance score as compared to the remaining sixty-three parameters. Furthermore, the statistical analysis results revealed that the Trigonometric Mean (TM) outperformed the other six statistical models. Moreover, based on the analysis of the parameters, specifically the M Quotient and FG index, it is evident that combining these parameters with any other parameter using various statistical models consistently produces excellent results in terms of the percentage score for returning awardees.

Secure Route-Obfuscation Mechanism with Information-Theoretic Security for Internet of Things.

As accessibility of networked devices becomes more and more ubiquitous, groundbreaking applications of the Internet of Things (IoT) find their place in many aspects of our society. The exploitation of these devices is the main reason for the cyberattacks in IoT networks. Security design is still an open problem and a crucial step in making IoT applications successful. In dicey environments, such as e-health, smart grid, and smart cities, real-time commands must reach the end devices in the scale of milliseconds. Traditional public-key cryptosystem, albeit necessary in the context of general Internet security, falls short in establishing new session keys in the scale of milliseconds for critical messages. In this paper, a systematic perspective for securing IoT communication, specifically satisfying the real-time constraint against certain adversaries in realistic settings. First, at the network layer, we propose a secret random route computation scheme using the software-defined network (SDN) based on a capability scheme using the network actions. The computed routes are random in the eyes of the eavesdropper. Second, at the application layer, the source breaks command messages into secret shares and sends them through the network to the destination. Only the legitimate destination device can reconstruct the command. The secret sharing scheme is efficient compared to PKI and comes with information-theoretic security against adversaries. Our proof formalizes the notion of security of the proposed scheme, and our simulations validate our design.